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	\title{10-Year Treasury Bond Yiels and Monte Carto Tree Search}
  

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\section{Abstract} As an important reference of investment, the yield of 10-Yr treasury bond reflects the situation and price trend of T-bond market entirely. An accurate prediction of it can be a reliable basement of investment strategy. However, it’s challenging and essential to obtain an accurate forecast of 10-year treasury bond. Here, we report an effective strategy which can give us a forecast of the yield of 10-Yr T-bond. All of Big Data, Neural Network and Monte Carlo Tree Search are applied to the yield prediction. With the basement of indicators selected by big data model, other two models can give us an optimized forecast of the yield of 10-Year T-bond. More detail will be introduced in the following article. We anticipate this model which contained many new technological methods can always give us the predicted value which is the closest prediction of Treasury bond yield.

\section{Research Methods} 
	\subsection{Research Framewrok}Based on the last 10 years economics, we dig deep into the details of how the ten-year bond yields change at each stage, and why. We separate the yield into variety parts use tree-like model, the model has three levels and each top nodes will be separated into different end nodes. The top nodes have three major parts: currency supply-demand relationship, inflation level and economic growth. In the second level, currency supply-demand relationship is separated into currency supply(M2) and social financing scale; inflation level is separated into CPI and PPI; economic growth is separated into GDP and PMI. In the end nodes, each economics indicator will be separated into quantifiable leading indicators.
	
	\subsection{Introduction of the Model}
Two main algorithms were applied in this part which are simulated annealing and genetic algorithm.
\\Simulated annealing (SA) is a probabilistic technique for approximating the global optimum of a given function. Specifically, it is a metaheuristic to approximate global optimization in a large search space.In our process, we assumed that we begin with a high tempreture and then add some random factors in the process.We would accept the better result in a certain probability so that we can get the optimized result.That is:
\\1. While $f(x_i)>f(x_{i+1})$, accept the new result $f(x_{i+1})$ instead of $f(x_i)$ ;
\\2. While $f(x_i)<f(x_{i+1})$, accept the new result $f(x_{i+1})$ instead of $f(x_i)$ in the certain probability;
\\   The certain probability is \\
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$p=exp(\frac{f(x_i)-f(x_{i+1})}{T})$
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What's more, genetic algorithm is important as well which simulates the process of natural selection that belongs to the larger class of evolutionary algorithms.\\
And we also use neural network to complete the procedure from step two to step three. Neural networks is computing systems inspired by the biological neural networks that constitute animal brains.We assume the all data set as the information our brain can get.And each connection between neurons can transmit a signal from one to another. The artificial neuron that receives the signal can process it and then signal artificial neurons connected to it.
\subsection{Application}
In the whole process, numerical indicators were our basement and selected by the computers automatically. The computer would check the signiture and importance of each indicaotors  in the step one and select some indicators which are significant and acting.   
		

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